Bibliographic record
Abstract
Expressive timing in hip-hop flow concerns the practice whereby an MC (rapper) inflects their flow rhythms on a minuscule scale not easily representable with standard musical notation—how far “ahead” or “behind” the beat they rap. Mitchell Ohriner (2019) positions expressive timing as an integral part of hip-hop flow and discusses it in detail. This paper complements his work by surveying flow timing across the broader hip-hop genre. Three broad practices of expressive timing in flow are identified. Swung timing subdivides the tactus unequally, similar to a common jazz drum timekeeping pattern. Lagging timing refers to the patterned delay of flow rhythm in relation to the underlying instrumental or sampled beat. And conversational timing pertains to flow performances that resemble rhythmic patterns idiomatic of spoken language. Theoretical and notational concepts developed by Fernando Benadon (2006, 2009) and Ohriner (2019) are used to illustrate the extent to which a flow performance involves these approaches to expressive timing, and propose analytical methods for these approaches that highlight their functional and rhetorical appeal. Expressive timing is investigated in light of Signifyin(g) in African American music (Samuel Floyd Jr., 2002), groove-based expressive microtiming (Vijay Iyer, 2002), Afrocentric models of rhetoric (Ronald Jackson, 1995), and narrativity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".